deep-research

deep-research is a skill for Claude Code from rp1-run/rp1. It costs 25 tokens per session (2,914 once invoked), scanned A, original, Apache-2.0.

A workflow for researching codebases or technical topics and producing a structured report. It divides exploration among agents, combines their findings, and then creates the report.

In plain words
What is it for?
Use it for broad technical investigations that need codebase exploration, parallel research, synthesis, and a final report.
Why use it?
It provides an organized path from research questions to a collected and documented result.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents.

Part of the rp1-base plugin — 20 skills, 17 agents, 1 hook shipped together

Good fit Use it for broad technical investigations that need codebase exploration, parallel research, synthesis, and a final report.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rp1-run/rp1/deep-research
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add rp1-run/rp1 --skill deep-research
Clone the repo
git clone --depth 1 https://github.com/rp1-run/rp1

Made for: Claude Code.

Or install rp1-base, the plugin that ships this one along with the rest of its 20 skills, 17 agents, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deep-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/rp1-run/rp1/deep-research/github.svg)](https://agentmods.dev/skills/rp1-run/rp1/deep-research)
Your own site
<a href="https://agentmods.dev/skills/rp1-run/rp1/deep-research"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/deep-research/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for deep-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/rp1-run/rp1/deep-research"><img src="https://agentmods.dev/badge/skills/rp1-run/rp1/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,914 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.02914
Opus 5 $0.00013 $0.01457
Sonnet 5 $0.00005 $0.00583
Haiku 4.5 $0.00003 $0.00291

Measured 10d ago against content hash b35f97e50b5e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

deep-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/base/skills/deep-research/SKILL.md · 378 lines

How it starts

The opening of the file, as written. The whole thing — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Research - Orchestration Command

You are executing the Deep Research workflow. You coordinate autonomous research through a map-reduce architecture: clarify intent, spawn parallel explorers, synthesize findings, and delegate report generation.

CRITICAL: Commands CAN spawn agents. You will spawn research-explorer agents for exploration and research-reporter for report generation. Do NOT delegate orchestration to another agent.

STATE-MACHINE

stateDiagram-v2
    [*] --> clarify
    clarify --> plan : intent_clear
    plan --> explore : plan_ready
    explore --> synthesize : exploration_complete
    synthesize --> report : synthesis_complete
    report --> [*] : done

On each phase transition, report via:

rp1 agent-tools emit \
  --workflow deep-research \
  --type status_change \
  --run-id {RUN_ID} \
  --name "Research: {brief summary of research topic}" \
  --step {CURRENT_STATE} \
  --data '{"status": "running"}'
  • RUN_ID comes from the generated Workflow Bootstrap section

State Progression Protocol:

  1. Report each --step with --data '{"status": "running"}' when you enter that state
  2. For non-terminal states: move to the NEXT state when done (entering the next state implies the previous completed)
  3. For terminal states (those with → [*] transitions): report with --data '{"status": "completed"}' and --close-run when the step's work finishes
  4. On error, transition to the appropriate failure state in the graph

Example sequence:

--step clarify --data '{"status": "running"}'       # entering clarify phase
--step plan --data '{"status": "running"}'          # intent clear, entering plan phase
--step explore --data '{"status": "running"}'       # plan ready, entering explore phase
--step synthesize --data '{"status": "running"}'    # exploration done, entering synthesize phase
--step report --data '{"status": "running"}'        # synthesis done, entering report phase
--step report --data '{"status": "completed"}' --close-run      # report work finished, workflow done

Read the full file on GitHub · 378 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 378 lines · 25 tokens per session scan A b35f97e50b5e

Subscribe to this mod's changes

deep-research is a skill published in the GitHub repository rp1-run/rp1 (38 stars, last pushed 2d ago), licensed Apache-2.0. It adds 25 tokens to every session and 2,914 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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